Smart imaging using artificial intelligence

ABSTRACT

Systems and methods are provided for determining a set of imaging parameters for an imaging system. A selection of an image is received from a set of images. A modification of certain quality measures is received for the selected image. The modified selected image is mapped to a set of imaging parameters of an imaging system based on the certain quality measures using a trained Deep Reinforcement Learning (DRL) agent.

BACKGROUND OF THE INVENTION

The present invention relates generally to medical imaging systems, andmore particularly to automatically determining parameters for medicalimaging systems using an intelligent artificial agent to optimize imagequality for a user.

Medical imaging systems are typically used by doctors and other medicalprofessions for clinical analysis and medical intervention procedures.The desired quality of the images generated by the medical imagingsystems depends on the subjective preferences of the doctor. Forexample, different doctors may prefer different amounts of sharpness,fuzziness, blurring, noise, dynamic range, contrast, smoothness,brightness, etc. in the images.

Conventionally, medical imaging systems are manually configured byadjusting its imaging parameters according to the preferences of thedoctor, the conditions of the patient, and the medical procedure beingperformed. Such manual configuration of such conventional medicalimaging systems is labor intensive, time consuming, and expensive due tothe numerous possible imaging parameters and their non-linearrelationship to the resulting quality of the medical images.

BRIEF SUMMARY OF THE INVENTION

In accordance with one embodiment, systems and methods are provided fordetermining a set of imaging parameters for an imaging system. Aselection of an image is received from a set of images. A modificationof certain quality measures is received for the selected image. Themodified selected image is mapped to a set of imaging parameters of animaging system based on the certain quality measures using a trainedDeep Reinforcement Learning (DRL) agent.

In accordance with one embodiment, a new image is generated using theimaging system configured with the set of imaging parameters. Thesubject being imaged may be continually monitored and the modifiedselected image may be mapped to an updated set of parameters of theimaging system using the trained DRL agent based on the monitoring.

In one embodiment, the modified selected image is mapped to the set ofparameters of the imaging system by generating a resulting image usingthe imaging system configured with the set of imaging parameters,comparing certain quality measures of the resulting image with thecertain quality measures of the modified selected image, and mapping theresulting image to an updated set of imaging parameters of the imagingsystem using the trained DRL agent.

The generating, the comparing, and the mapping the resulting image maybe iteratively repeated using the respective updated set of imagingparameters until the comparing satisfies a threshold. In one embodiment,comparing the certain quality measures of the resulting image with thecertain quality measures of the modified selected image may be performedby quantifying values of the certain quality measures for the resultingimage (e.g., using deep learning based methods) and comparing thequantified values of the certain quality measures for the resultingimage with the certain quality measures of the modified selected image.

In one embodiment, the certain quality measures are determined from aset of quality measures for one or more users based on actions of theone or more users on one or more given images using a deep inversereinforcement learning (DIRL) based method. For example, the actions mayinclude selecting an image, weighting an image, modifying an image, etc.The values of the set of quality measures for the one or more givenimages may be quantified using a trained deep learning based method.

In one embodiment, the one or more quality measures include at least oneof sharpness, fuzziness, blurring, noise, dynamic range, contrast,smoothness, and brightness.

These and other advantages of the invention will be apparent to those ofordinary skill in the art by reference to the following detaileddescription and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary high level workflow for determining parametersof a medical imaging system for optimizing image quality for a user, inaccordance with one or more embodiments;

FIG. 2 shows an exemplary system configured for determining a set ofparameters of a medical imaging system for optimizing image quality fora user, in accordance with one or more embodiments;

FIG. 3 shows a method for determining a set of imaging parameters of amedical imaging system for optimizing image quality for a user, inaccordance with one or more embodiments;

FIG. 4 shows a method for mapping an image to a set of imagingparameters; and

FIG. 5 shows a high-level block diagram of a computer for determining aset of parameters of a medical imaging system for optimizing imagequality for a user, in accordance with one or more embodiments.

DETAILED DESCRIPTION

The present invention generally relates to determining parameters ofmedical imaging systems to optimize image quality for a user.Embodiments of the present invention are described herein to give avisual understanding of methods for determining parameters of medicalimaging systems. A digital image is often composed of digitalrepresentations of one or more objects (or shapes). The digitalrepresentation of an object is often described herein in terms ofidentifying and manipulating the objects. Such manipulations are virtualmanipulations accomplished in the memory or other circuitry/hardware ofa computer system. Accordingly, it is to be understood that embodimentsof the present invention may be performed within a computer system usingdata stored within the computer system.

Further, it should be understood that while the embodiments discussedherein may be discussed with respect to medical imaging systems forimaging a patient, the present invention is not so limited. Embodimentsof the present invention may be applied for determining parameters forconfiguring any imaging system (e.g., cameras and video recordingdevices) for imaging any subject.

FIG. 1 shows a high level workflow 100 for determining parameters of amedical imaging system for optimizing image quality for a user, inaccordance with one or more embodiments. In workflow 100, intelligentartificial agent 102 receives a user selection of a target image 104from a set of images. Target image 104 may be selected by a doctor,medical professional, or any other user based on his or her subjectivepreferences. Target image 104 may be further modified by the user basedon his or her subjective preferences. Agent 102 applies machine learningalgorithms trained to map the modified target image 104 to a set ofparameters of a medical imaging system. The medical imaging system maybe configured with the determined set of parameters to generate acurrent image 106 having a quality similar to that of the modifiedtarget image 104.

FIG. 2 shows a system 200 configured for determining a set of parametersof a medical imaging system for optimizing image quality for a user, inaccordance with one or more embodiments. System 100 includes workstation202, which may be used for assisting a user (e.g., a doctor, clinician,or any other medical professional) during a procedure, such as, e.g., apatient examination. Workstation 202 includes one or more processors 206communicatively coupled to memory 204, display device 208, andinput/output devices 210. Memory 204 may store a plurality of modulesrepresenting functionality of workstation 202 performed when executed onprocessor 206. It should be understood that workstation 202 may alsoinclude additional elements, such as, e.g., a communications interface.

In one embodiment, an intelligent artificial agent 220 is implemented onworkstation 202 to determine parameters of medical imaging system 212for optimizing image quality for a user. Agent 220 may be implemented ascomputer program instructions (e.g., code), which may be loaded intomemory 204 and executed by processor 206. In one embodiment, agent 220implemented on workstation 202 in FIG. 2 is agent 102 in FIG. 1.

Workstation 202 may assist the clinician in imaging subject 218 (e.g., apatient) for a medical procedure. While subject 218 is described hereinas being a patient, it should be understood that subject 218 may includeany object (e.g., any person, place, or thing). Workstation 202 mayreceive medical imaging data generated by medical imaging system 212.Medical imaging system 212 may be any modality, e.g., magnetic resonanceimaging (MRI), computed tomography (CT), ultrasound (US), single-photonemission computed tomography (SPECT), positron emission tomography(PET), or any other suitable modality, or any combination of modalities.

In some embodiments, medical imaging system 212 may employ one or moreprobes 216 for imaging subject 218. Probe 216 may be instrumented withone or more devices (not shown) for performing the medical procedure.The devices instrumented on probe 216 may include, for example, imagingdevices, tracking devices, insufflation devices, incision devices,and/or any other suitable device. Medical imaging system 212 iscommunicatively coupled to probe 216 via connection 214, which mayinclude an electrical connection, an optical connection, a connectionfor insufflation (e.g., conduit), or any other suitable connection.

The desired quality of the images generated by medical imaging system212 depends on the subjective preferences of the user, and may also bebased on subject 218 and the medical procedure being performed. Togenerate images having a quality according to the subjective preferencesof the user from medical imaging system 212, parameters of medicalimaging system 212 must be set before an exam or procedure is performed.For example, in one embodiment, where medical imaging system 212 is anMRI imaging system, the parameters of the MRI imaging system may includerepetition time (TR) and echo time (TE). In another example, theparameters may include energy level for computed tomography (CT), x-ray,and conebeam CT imaging systems. Post-processing parameters may include,e.g., imaging smoothing, image sharpening, and noise reduction.

Advantageously, agent 220 applies machine learning methods (e.g., deeplearning based methods) to map a target image, selected and modified inaccordance with the user's subjective preferences, to a set of imagingparameters for medical imaging system 212. In this manner, medicalimaging system 212 configured with the determined set of imagingparameters may generate new images having a quality in accordance withthe subject preferences of the user. Agent 220 in accordance withembodiments of the invention thus provides for improvements in computerrelated technology by automatically tuning the imaging parameters ofmedical imaging system 212 to thereby generate new images optimizedaccording to the subjective preferences of the user. Agent 220 avoidsthe time consuming, labor intensive, and ad hoc manual configuration ofthe imaging parameters of medical imaging system 212, thereby reducingtime and expense in configuring medical imaging system 212.

FIG. 3 shows a method 300 for determining a set of imaging parameters ofa medical imaging system for optimizing image quality for a user, inaccordance with one or more embodiments. Method 300 will be discussedwith respect to system 200 of FIG. 2. In one embodiment, agent 220implemented on workstation 202 of FIG. 2 performs the steps of method300 of FIG. 3.

At step 302, values for a set of quality measures are quantified for oneor more given images. The quantified values for the set of qualitymeasures represent the subjective or perceptual appearance for the givenimage. For example, the quality measures may include various levels ofsharpness, fuzziness, blurring, noise, dynamic range, contrast,smoothness, brightness, and/or any other image attribute or combinationsof image attributes. The given image may be any suitable image. In oneembodiment, the given image is a medical image (e.g., an x-ray image ormagnetic resonance image) of a target region on interest with desiredquality measures (e.g., image contrast, brightness, noise level, andfield of view). In one embodiment, step 302 is a pre-processing stepthat is performed once.

Values for the set of quality measures for the given image may bequantified using deep-learning based methods to map the given image tothe values for the set of quality measures. A deep-learning network istrained from a database of training images associated with variousqualities. The training images are labelled with a corresponding values(or levels) for each quality measure in the set of quality measures. Inone embodiment, training images are patient images labeled by applyingmathematical definitions or calculations and confirmed or modified byusers (e.g., experts). In another embodiment, the training images mayalternatively or additionally be synthetic images which are generated bysimulating the medical imaging system with known values for the set ofquality measures. Once the deep-learning network is trained, it isapplied to the given image to quantify the set of quality measures.

At step 304, certain quality measures are determined or identified fromthe set of quality measures. The certain quality measures may be thequality measures in the set of quality measures that are the mostinfluential. In one embodiment, step 304 is a pre-processing step thatis performed once for a group of users to provide for a general set ofthe certain quality measures for a given type of image modality (e.g.,x-ray, ultrasound, computed tomography, magnetic resonance imaging,etc.). In another embodiment, step 304 may be performed for eachparticular user to provide for a specific set of the certain qualitymeasures for that particular user. In this embodiment, the user may bethe same user associated with step 306.

The certain quality measures may be determined using, e.g., a deepinverse reinforcement learning (DIRL) based method. In DIRL, no rewardfunction is provided. The goal of DIRL is to learn the reward functionby observing the behavior of an agent assumed to be behaving optimally(i.e., in accordance with a policy). The reward function represents apolicy for performing an action in view of a goal. The reward functionassigns a reward for each action based on the effect of that action onthe goal. For example, a higher reward (positive reward) is assigned foractions that lead towards the accomplishment of the goal while a lowerreward (negative reward) is assigned to actions that do not lead towardsthe accomplishment of the goal.

The DIRL based method observes actions of the user (or group of users)to learn the reward function. The user actions may include usermodifications of the quantified values of the given images according tothe subjective preferences of the user or users. In some embodiments,the user actions may additionally or alternatively include userselections of the quantified given images from a set of images,weighting the quantified given images according to the subjectivepreferences of the user, or any other suitable user action. Themodifications of the quantified values of the given images are of one ormore quality measures of the set of quality measures, e.g., sharpness,fuzziness, blurring, noise, dynamic range, contrast, smoothness,brightness, and/or any other image attribute of the selected image.

At step 306, a user-selected image is mapped to a set of imagingparameters based on the certain quality measures. In one embodiment,step 306 is performed by performing method 400 of FIG. 4.

FIG. 4 shows a method 400 for mapping a user-selected image to a set ofimaging parameters of a medical imaging system, in accordance with oneor more embodiments. Accordingly, the medical imaging system configuredwith the set of imaging parameters generates images with a qualitymeasure in accordance with the subjective preferences of the user.

At step 402, a selection of an image is received from a user from a setof images. The user may be any user, such as, e.g., a doctor or amedical professional. The selection may be received using display 208and/or input/output device 210 of FIG. 2. The set of images may includeone or more images of various qualities stored in memory 204. Forexample, the various qualities of the set of images may include variouslevels of sharpness, fuzziness, blurring, noise, dynamic range,contrast, smoothness, brightness, and/or any other image attribute orcombinations of image attributes. The selection of the image from theset of images reflects or is based on the subjective preferences of theuser. In one embodiment, the selection of the image is also based on theprocedure being performed and the subject 218 (e.g., the patient) of theprocedure. For example, if an MR scan is being acquired to detectlesions, then the image may be selected from the set of images based onits ability to detection lesions. While the image quality maytraditionally be considered poor in this example, it could be optimizedfor the diagnostic task that the scan is being performed for.

The set of images may include actual medical images of patients acquiredfrom medical imaging system 212 or any other suitable medical imagingsystem of a same or different modality (e.g., CT, MR, x-ray, ultrasound,PET, etc.). The set of images may be acquired from the same patient asnew images generated from medical imaging system 212 during an onlinephase, and may be generated at different times and from differentpatients. The set of images can be obtained by receiving the imagesdirectly from medical imaging system 212 or by loading previouslyacquired images from a storage or memory of a computer system. In someembodiments, the set of images can include synthetic images which aregenerated by simulating the medical imaging system.

In one embodiment, a plurality of images may be selected from the set ofimages by the user. The user may assign or associate a weight to eachrespective selected imaging indicating the subjective preferences of theuser for that respective selected image.

At step 404, modifications of the selected image are received from theuser according to the subjective preferences of the user. Themodifications of the selected image may include edits or refinements ofthe selected image that alter the appearance of the selected imageaccording to the most influential quality measures. In one embodiment,the certain quality measures that are modified at step 404 are thecertain quality measures (e.g., the most influential quality measures)determined in step 304 of FIG. 3. For example, the certain qualitymeasures may include sharpness, fuzziness, blurring, noise, dynamicrange, contrast, smoothness, brightness, and/or any other imageattribute of the selected image.

At step 406, the modified selected image is mapped to a set ofparameters of medical imaging system 212 using a trained DeepReinforcement Learning (DRL) agent. The DRL agent is trained in anoffline or training stage to generate the set of parameters (i.e., oneor more parameters) for configuring medical imaging system 212 such thatnew images generated by medical imaging system 212 so configured have aquality in accordance with (i.e., similar to) the subjective preferencesof the user. In one embodiment, the DRL agent employs a Deep NeuralNetwork (DNN) trained using a supervised DRL technique. ReinforcementLearning (RL) is a type of machine learning in which a software basedartificial agent uses reward feedback to automatically learn idealbehavior in a specific context and for a specific task. In DRL, whichcombines DNNs with RL, a policy learning process is formulated as an RLproblem and the action-value function is estimated as an iterativeupdate. In DRL, the training of the agent is typically unguided and theagent is free to evolve in its environment according to its currentpolicy estimate.

In an advantageous embodiment of the present invention, the DRL trainingof the DNN is supervised based on training images annotated with knownground truth parameters of medical imaging systems. The training imagesmay be imaging data of a same or different subject, taken by a same ordifferent medical imaging system that generates the imaging data in theonline phase. In one embodiment, the training images may includesynthetic imaging data generated by simulating a medical imaging system.The training images may be annotated with a policy goal and actions(that lead towards or away from the policy goal). The DRL agent istrained by letting the agent repeatedly take actions to adjust theimaging parameters and collect the produced rewards. During theseactions, the agent establishes the connection between the imagingparameters and the associated rewards. In one embodiment, the rewardfunction used in the training phase may be, e.g., the sum of the squareddifference between the quality measures of the current image and thetarget image, and its variations. Once the agent is trained, it can beapplied to adjust the imaging parameter on a new image to maximize thereward.

In one embodiment, the DRL agent maps the modified selected image to theset of imaging parameters and generates a resulting image using thedetermined set of parameters. The certain quality measures of theresulting image are then compared to the certain quality measures of themodified selected image. For example, the certain quality measures ofthe resulting image may be quantified using deep learning based methodsand compared with the known values of the certain quality measures ofthe modified selected image. If the comparison of the certain qualitymeasures of the resulting image and the modified selected image does notsatisfy a (e.g., predetermined) threshold, the DRL agent maps theresulting image to a new set of imaging parameters. This step may beiteratively repeated until the threshold is satisfied, indicating thatthe set of imaging parameters generate images that have sufficientlysimilar certain quality measures as the modified selected image.

In one embodiment, the DRL agent maps the modified selected image to theset of imaging parameters in a single step, for a given size or anatomyof subject 218.

At step 408, a new image is generated using the medical imaging systemconfigured with the determined set of imaging parameters. The new imagewill advantageously have a quality in accordance with (e.g., similar to)the user's subject preferences.

At step 410, the medical procedure is continually monitored for changesto generate an updated set of imaging parameters. For example,conditions such as, e.g., size of subject 218, the procedure beingperformed, the devices being used) may alter the set of parameters forgenerating optimized images for the user.

Systems, apparatuses, and methods described herein may be implementedusing digital circuitry, or using one or more computers using well-knowncomputer processors, memory units, storage devices, computer software,and other components. Typically, a computer includes a processor forexecuting instructions and one or more memories for storing instructionsand data. A computer may also include, or be coupled to, one or moremass storage devices, such as one or more magnetic disks, internal harddisks and removable disks, magneto-optical disks, optical disks, etc.

Systems, apparatus, and methods described herein may be implementedusing computers operating in a client-server relationship. Typically, insuch a system, the client computers are located remotely from the servercomputer and interact via a network. The client-server relationship maybe defined and controlled by computer programs running on the respectiveclient and server computers.

Systems, apparatus, and methods described herein may be implementedwithin a network-based cloud computing system. In such a network-basedcloud computing system, a server or another processor that is connectedto a network communicates with one or more client computers via anetwork. A client computer may communicate with the server via a networkbrowser application residing and operating on the client computer, forexample. A client computer may store data on the server and access thedata via the network. A client computer may transmit requests for data,or requests for online services, to the server via the network. Theserver may perform requested services and provide data to the clientcomputer(s). The server may also transmit data adapted to cause a clientcomputer to perform a specified function, e.g., to perform acalculation, to display specified data on a screen, etc. For example,the server may transmit a request adapted to cause a client computer toperform one or more of the method steps described herein, including oneor more of the steps of FIGS. 3 and 4. Certain steps of the methodsdescribed herein, including one or more of the steps of FIGS. 3 and 4,may be performed by a server or by another processor in a network-basedcloud-computing system. Certain steps of the methods described herein,including one or more of the steps of FIGS. 3 and 4, may be performed bya client computer in a network-based cloud computing system. The stepsof the methods described herein, including one or more of the steps ofFIGS. 3 and 4, may be performed by a server and/or by a client computerin a network-based cloud computing system, in any combination.

Systems, apparatus, and methods described herein may be implementedusing a computer program product tangibly embodied in an informationcarrier, e.g., in a non-transitory machine-readable storage device, forexecution by a programmable processor; and the method steps describedherein, including one or more of the steps of FIGS. 3 and 4, may beimplemented using one or more computer programs that are executable bysuch a processor. A computer program is a set of computer programinstructions that can be used, directly or indirectly, in a computer toperform a certain activity or bring about a certain result. A computerprogram can be written in any form of programming language, includingcompiled or interpreted languages, and it can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment.

A high-level block diagram 500 of an example computer that may be usedto implement systems, apparatus, and methods described herein isdepicted in FIG. 5. Computer 502 includes a processor 504 operativelycoupled to a data storage device 512 and a memory 510. Processor 504controls the overall operation of computer 502 by executing computerprogram instructions that define such operations. The computer programinstructions may be stored in data storage device 512, or other computerreadable medium, and loaded into memory 510 when execution of thecomputer program instructions is desired. Thus, the method steps ofFIGS. 3 and 4 can be defined by the computer program instructions storedin memory 510 and/or data storage device 512 and controlled by processor504 executing the computer program instructions. For example, thecomputer program instructions can be implemented as computer executablecode programmed by one skilled in the art to perform the method steps ofFIGS. 3 and 4. Accordingly, by executing the computer programinstructions, the processor 504 executes the method steps of FIGS. 3 and4. Computer 504 may also include one or more network interfaces 506 forcommunicating with other devices via a network. Computer 502 may alsoinclude one or more input/output devices 508 that enable userinteraction with computer 502 (e.g., display, keyboard, mouse, speakers,buttons, etc.).

Processor 504 may include both general and special purposemicroprocessors, and may be the sole processor or one of multipleprocessors of computer 502. Processor 504 may include one or morecentral processing units (CPUs), for example. Processor 504, datastorage device 512, and/or memory 510 may include, be supplemented by,or incorporated in, one or more application-specific integrated circuits(ASICs) and/or one or more field programmable gate arrays (FPGAs).

Data storage device 512 and memory 510 each include a tangiblenon-transitory computer readable storage medium. Data storage device512, and memory 510, may each include high-speed random access memory,such as dynamic random access memory (DRAM), static random access memory(SRAM), double data rate synchronous dynamic random access memory (DDRRAM), or other random access solid state memory devices, and may includenon-volatile memory, such as one or more magnetic disk storage devicessuch as internal hard disks and removable disks, magneto-optical diskstorage devices, optical disk storage devices, flash memory devices,semiconductor memory devices, such as erasable programmable read-onlymemory (EPROM), electrically erasable programmable read-only memory(EEPROM), compact disc read-only memory (CD-ROM), digital versatile discread-only memory (DVD-ROM) disks, or other non-volatile solid statestorage devices.

Input/output devices 508 may include peripherals, such as a printer,scanner, display screen, etc. For example, input/output devices 508 mayinclude a display device such as a cathode ray tube (CRT) or liquidcrystal display (LCD) monitor for displaying information to the user, akeyboard, and a pointing device such as a mouse or a trackball by whichthe user can provide input to computer 502.

Any or all of the systems and apparatus discussed herein, includingelements of agent 102 of FIG. 1 and workstation 202 of FIG. 2, may beimplemented using one or more computers such as computer 502.

One skilled in the art will recognize that an implementation of anactual computer or computer system may have other structures and maycontain other components as well, and that FIG. 5 is a high levelrepresentation of some of the components of such a computer forillustrative purposes.

The foregoing Detailed Description is to be understood as being in everyrespect illustrative and exemplary, but not restrictive, and the scopeof the invention disclosed herein is not to be determined from theDetailed Description, but rather from the claims as interpretedaccording to the full breadth permitted by the patent laws. It is to beunderstood that the embodiments shown and described herein are onlyillustrative of the principles of the present invention and that variousmodifications may be implemented by those skilled in the art withoutdeparting from the scope and spirit of the invention. Those skilled inthe art could implement various other feature combinations withoutdeparting from the scope and spirit of the invention.

1. A method for determining a set of imaging parameters for an imagingsystem, comprising: receiving a selection of an image from a set ofimages; receiving a modification of certain quality measures for theselected image; and mapping the modified selected image to a set ofimaging parameters of an imaging system based on the certain qualitymeasures using a trained Deep Reinforcement Learning (DRL) agent.
 2. Themethod of claim 1, further comprising: generating a new image using theimaging system configured with the set of imaging parameters.
 3. Themethod of claim 1, where mapping the modified selected image to a set ofimaging parameters of an imaging system based on the certain qualitymeasures using a trained Deep Reinforcement Learning (DRL) agentcomprises: generating a resulting image using the imaging systemconfigured with the set of imaging parameters; comparing certain qualitymeasures of the resulting image with the certain quality measures of themodified selected image; and mapping the resulting image to an updatedset of imaging parameters of the imaging system using the trained DRLagent.
 4. The method of claim 3, further comprising: repeating thegenerating, the comparing, and the mapping the resulting image using therespective updated set of imaging parameters until the comparingsatisfies a threshold.
 5. The method of claim 3, wherein comparingcertain quality measures of the resulting image with the certain qualitymeasures of the modified selected image comprises: quantifying values ofthe certain quality measures for the resulting image; and comparing thequantified certain quality measures for the resulting image with thecertain quality measures of the modified selected image.
 6. The methodof claim 1, further comprising: determining certain quality measuresfrom a set of quality measures for one or more users based on actions ofthe one or more users on one or more given images using a deep inversereinforcement learning (DIRL) based method.
 7. The method of claim 6,further comprising: quantifying values of the set of quality measuresfor the one or more given images using a trained deep learning basedmethod.
 8. The method of claim 1, wherein the one or more qualitymeasures include at least one of sharpness, fuzziness, blurring, noise,dynamic range, contrast, smoothness, and brightness.
 9. The method ofclaim 1, further comprising: continually monitoring a subject beingimaged; and mapping the modified selected image to an updated set ofimaging parameters of the imaging system using the trained DRL agentbased on the monitoring.
 10. An apparatus for determining a set ofimaging parameters for an imaging system, comprising: means forreceiving a selection of an image from a set of images; means forreceiving a modification of certain quality measures for the selectedimage; and means for mapping the modified selected image to a set ofimaging parameters of an imaging system based on the certain qualitymeasures using a trained Deep Reinforcement Learning (DRL) agent. 11.The apparatus of claim 10, further comprising: means for generating anew image using the imaging system configured with the set of imagingparameters.
 12. The apparatus of claim 10, where the mapping themodified selected image to a set of imaging parameters of an imagingsystem based on the certain quality measures using a trained DeepReinforcement Learning (DRL) agent comprises: means for generating aresulting image using the imaging system configured with the set ofimaging parameters; means for comparing certain quality measures of theresulting image with the certain quality measures of the modifiedselected image; and means for mapping the resulting image to an updatedset of imaging parameters of the imaging system using the trained DRLagent.
 13. The apparatus of claim 12, further comprising: means forrepeating the generating, the comparing, and the mapping the resultingimage using the respective updated set of imaging parameters until thecomparing satisfies a threshold.
 14. The apparatus of claim 12, whereinthe means for comparing certain quality measures of the resulting imagewith the certain quality measures of the modified selected imagecomprises: means for quantifying values of the certain quality measuresfor the resulting image; and means for comparing the quantified certainquality measures for the resulting image with the certain qualitymeasures of the modified selected image.
 15. A non-transitory computerreadable medium storing computer program instructions for determining aset of imaging parameters for an imaging system, the computer programinstructions when executed by a processor cause the processor to performoperations comprising: receiving a selection of an image from a set ofimages; receiving a modification of certain quality measures for theselected image; and mapping the modified selected image to a set ofimaging parameters of an imaging system based on the certain qualitymeasures using a trained Deep Reinforcement Learning (DRL) agent. 16.The non-transitory computer readable medium of claim 15, the operationsfurther comprising: generating a new image using the imaging systemconfigured with the set of imaging parameters.
 17. The non-transitorycomputer readable medium of claim 15, further comprising: determiningcertain quality measures from a set of quality measures for one or moreusers based on actions of the one or more users on one or more givenimages using a deep inverse reinforcement learning (DIRL) based method.18. The non-transitory computer readable medium of claim 17, furthercomprising: quantifying values of the set of quality measures for theone or more given images using a trained deep learning based method. 19.The non-transitory computer readable medium of claim 15, wherein the oneor more quality measures include at least one of sharpness, fuzziness,blurring, noise, dynamic range, contrast, smoothness, and brightness.20. The non-transitory computer readable medium of claim 15, furthercomprising: continually monitoring a subject being imaged; and mappingthe modified selected image to an updated set of imaging parameters ofthe imaging system using the trained DRL agent based on the monitoring.